Augmented Lagrangian method for TV-l1-l2 based colour image restoration

被引：31

作者：

Padcharoen, Anantachai ^{[1
]}

Kumam, Poom ^{[1
,2
]}

Martinez-Moreno, Juan ^{[3
]}

机构：

[1] KMUTT, Fac Sci, Dept Math, KMUTTFixed Point Res Lab,Fixed Point Lab, Room SCL 802,Sci Lab Bldg,126 Pracha Uthit Rd, Bangkok 10140, Thailand

[2] KMUTT, Fac Sci, Theoret & Computat Sci Ctr TaCS, KMUTT Fixed Point Theory & Applicat Res Grp, Sci Lab Bldg,126 Pracha Uthit Rd, Bangkok 10140, Thailand

[3] Univ Jaen, Dept Math, Fac Expt Sci, Campus Las Lagunillas S-N, Jaen 23071, Spain

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2019年 / 354卷

关键词：

Augmented Lagrangian; Total variation(TV); Image restoration; Convex minimization problem; Image recovery problems;

D O I：

10.1016/j.cam.2018.09.053

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we introduce a total variation 11-12 regularization scheme with adapting the parameter for image restoration involving blurry and noisy colour images. Numerically, an efficient augmented Lagrangian method associated with alternating minimization method is described to obtain the optimal solution recursively. We provide the convergence analysis for the resulting algorithm. Experimental results show that our proposed model and algorithm have good signal to noise ratio (SNR) and improvement in signal to noise ratio (ISNR) values for a motion blur with different kinds of noises. (C) 2018 Elsevier B.V. All rights reserved.

引用

页码：507 / 519

页数：13

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